Artificial Intelligence Jobs in Santa Clara, CA

Design and implement intelligent automation for AV software development, testing, release, and operational workflows. Build CI/CD pipelines integrating simulation and hardware-in-loop testing, develop engineering data pipelines and metrics, and create AI-enabled tools for test analysis, failure classification, defect triage, reporting, and workflow orchestration. Establish evaluation, governance, security, and monitoring standards for AI solutions while improving release readiness, software quality, engineering productivity, and cross-functional visibility.

Architects and delivers enterprise-scale AI security systems for identity risk, anomalous access, malware detection, sensitive data classification, and threat recognition. Builds inference engines, ML platforms, feature pipelines, and production model-monitoring workflows. Provides technical leadership across multi-quarter initiatives, integrates AI with access-control and security infrastructure, evaluates emerging techniques such as LLMs and agentic systems, and collaborates across identity, threat detection, and platform teams.

Develop and deploy machine learning models, datasets, algorithms, and training recipes to improve AI safety for multimodal LLMs and agentic systems. Work across LLM security, frontier risks, agentic safety, multi-turn evaluation, content safety, hallucinations, and fairness. Apply post-training methods including SFT and RLHF/RLAIF, conduct safety evaluations and research, and collaborate cross-functionally with engineering, data science, and research teams.

Supports attorneys and business professionals using the firm's AI platforms. Provides one-on-one workflow coaching, troubleshoots prompting and tool-use questions, creates reference guides, performs comparative and feature testing across Claude, Harvey, and Legora, documents findings, and implements governance and token-management guardrails. The role requires strong communication, structured documentation, end-user support experience, and interest in legal technology and applied AI.

Embed with customer AI teams to design, prototype, and productionize AI agents using W&B Weave. Advise on architecture, build reference implementations and demos, run technical workshops, gather field feedback for product roadmap, and troubleshoot customer environments.

Develop and deploy machine learning datasets, models, evaluation systems, and MLOps tools focused on content safety, AI security, robustness, fairness, and responsible behavior for multimodal LLMs. Research bias detection and mitigation, define safety metrics, improve model monitoring and automation, and collaborate with engineering, data science, and research teams. The role also involves addressing harmful content, misinformation, hallucinations, adversarial robustness, and GenAI security.

Own Jetson’s developer experience, system performance, competitive intelligence, and product roadmap for edge AI and robotics. Lead requirements and release readiness across real-time execution, low-latency networking, RDMA, memory optimization, profiling tools, SDK usability, onboarding, documentation, and sample code. Partner with engineering, customers, partners, ISVs, and developers to prioritize performance-critical features and strengthen NVIDIA’s platform leadership.

Design and implement real-time localization, ego-motion tracking, and state-estimation systems by fusing multi-modal sensors. Build scalable offline pose-tracking pipelines, multi-sensor calibration tools, and evaluation/testing frameworks. Collaborate cross-functionally to deploy reliable, high-performance software for humanoid robotics in real-world environments.

Lead the definition, development, launch, and adoption of AI agents for regulated financial-services workflows. Identify use cases, translate complex banking and payments processes into agent requirements, define human and system interactions, create prototypes and evaluation criteria, and partner with engineering on integrations, controls, monitoring, and production readiness. Drive client validation, releases, measurement, and continuous improvement using operational evidence and business outcomes.

Architects, operates, and improves cloud data and analytics platforms and BI infrastructure. Owns platform standards, CI/CD, reliability, monitoring, incident response, security, governance, performance, and technical documentation. Leads onboarding and enables engineering and client teams through reusable patterns and guardrails. Requires production platform ownership, cloud data and BI expertise, accounting experience, scripting, SQL, troubleshooting, and cloud fundamentals.

Develop and execute public relations campaigns for NVIDIA’s enterprise AI models. Translate complex AI research and open-source model capabilities into compelling narratives, press releases, and media stories. Build relationships with reporters, researchers, partners, executives, and internal teams; secure meaningful technology coverage; coach teams on messaging; and coordinate globally to promote AI model innovation.

Build and ship production agentic systems for Roku’s OS Platform UI, including multi-step agents, internal engineering tools, real-time telemetry processing, and hardware-in-the-loop capabilities. The role requires strong systems or infrastructure fundamentals and ownership of AI-generated code through design, review, and quality gates. Engineers work across platform, application, UI, cloud, and physical device environments at streaming scale.

The AppSec/DevSecOps Engineer embeds security across the software development lifecycle, implementing SAST, DAST, SCA, secrets, container, API, and infrastructure-as-code controls in CI/CD pipelines. Responsibilities include threat modeling, secure architecture and code reviews, cloud and software supply-chain security, vulnerability assessment and remediation, security automation, dashboards, risk reporting, developer training, and secure SDLC improvement across Azure, AWS, Kubernetes, and hybrid environments.

Architect and develop scalable, event-driven backend services, APIs, orchestration systems, and data-context management solutions for AI agents supporting laboratory workflows. Integrate cloud infrastructure, scientific instruments, and software systems while establishing data contracts, integration standards, observability, security, and resilience. Prototype emerging AI technologies and mature successful solutions for regulated production environments. The role requires extensive distributed systems experience and supports R&D software teams in San Jose or Indianapolis.

Lead the design and hands-on development of shared AI agent platforms for Cisco and Splunk security products. Build orchestration, evaluation, tracing, governance, memory, and tool-use systems for Agentic SOC capabilities across cloud, on-premises, sovereign, and air-gapped environments. Define AI quality metrics, improve production reliability and safety, ensure authorization and auditability, diagnose cross-system failures, influence technical direction, and mentor senior engineers.

Lead the development of large-scale video foundation models for humanoid autonomy, focusing on training strategies and model evaluation for real-world applications.

Develop and optimize GPU-accelerated numerical linear algebra libraries for AI and high-performance computing. Design kernel generation, JIT compilation, matrix operations, and operator fusions; lead projects involving multiple engineers and teams; provide technical mentorship; collaborate with product management and customers on requirements and roadmaps; and improve performance and maintainability through architectural enhancements.

Own full sales and partnership cycle to bring voice AI to mobile service robots: engage senior robotics decision-makers, qualify and prioritize opportunities, close deals, and shape the commercial playbook while operating autonomously within a small technical team.

Design and build production tools, content pipelines, DCC integrations, and AI-enabled agentic workflows for creative production teams. Responsibilities include architecture, API development, testing, monitoring, release management, production support, and workflow automation. The role partners with artists and engineers, mentors team members, and leads technical decisions. Required experience includes 8+ years of software engineering, 2+ years of technical leadership, strong Python skills, DCC and content pipeline integration, and reliable multi-step AI workflow development.

Lead a team of AI engineers building production AI applications, agents, automation, retrieval pipelines, and model integrations for Finance. Partner with CFO organization functions to identify high-value use cases, set engineering standards, review designs and code, evaluate external AI vendors, run proofs of concept, and make build-versus-buy recommendations. Scale adoption through reusable AI components, technical patterns, and development standards.

Lead a data science team building scalable AI and machine learning solutions for customer-facing financial products. Develop and fine-tune NLP and large language models, manage model training, evaluation, validation, and production deployment, and partner with engineering, product, and design teams. Apply emerging generative AI technologies at scale while mentoring talent, translating technical findings into business outcomes, and delivering resilient systems for millions of customers.

Lead data science for AI Foundations, developing and deploying large-scale NLP, machine learning, and generative AI solutions. Partner with software engineering, ML engineering, product, and design teams to build customer-facing applications. Train, fine-tune, evaluate, and operationalize language models and other advanced models using technologies including PyTorch, AWS, Hugging Face, LangChain, and VectorDBs. Provide technical leadership, communicate complex findings, and develop team talent.

Leads firmwide adoption of AI tools in partnership with attorneys and paralegals. Translates legal workflows into scalable AI solutions, manages platform configuration, supports AI governance and risk practices, and develops communications and training programs. The role requires hands-on AI platform expertise, strong project management, legal workflow knowledge, stakeholder collaboration, and the ability to identify and address risks in AI-enabled legal work.

Leads the design, evaluation, deployment, and monitoring of production AI systems across generative AI, agentic AI, computer vision, forecasting, and optimization. Establishes rigorous metrics for accuracy, uncertainty, bias, hallucinations, drift, explainability, and failure modes; conducts experiments and communicates business impact, risks, and trade-offs. Builds solutions using foundation models, RAG, agentic workflows, unstructured data, and MLOps practices.

Tenure-track Assistant Professor of Marketing responsible for conducting high-quality quantitative marketing research, publishing in leading journals, teaching undergraduate and graduate marketing courses, and contributing service to the department, university, profession, or community. Preferred research areas include branding, advertising, or health marketing with applications of artificial intelligence.